In this paper we propose a new model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. We do this by first characterizing what sets team cognition and collectively intelligence apart, and then reviewing the literature on “superforecasting” and the ability for effectively coordinated teams to outperform predictions by large groups. Lastly, we delve into the ways in which teamwork can be enhanced by artificial intelligence through our model, finally highlighting the many areas of research worth exploring through interdisciplinary efforts.
Get full access to this article
View all access options for this article.
References
1.
AtanasovPavel, et al. “The marketcast method for aggregating prediction market forecasts.”International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, Berlin, Heidelberg, 2013.
2.
AtanasovP.RescoberP.StoneE.SwiftS. A.Servan-SchreiberE.TetlockP.… MellersB. (2016). Distilling the wisdom of crowds: Prediction markets vs. prediction polls. Management science, 63(3), 691-706.
3.
Cannon-BowersJ. A.SalasE.ConverseS. A. (1990). Cognitive psychology and team training: Training shared mental models and complex systems. Human factors society bulletin, 33(12), 1-4.
4.
CookeN. J.GormanJ. C.WinnerJ. L.DursoF. T. (2007). Team cognition. Handbook of applied cognition, 2, 239-268.
5.
CookeN. J.GormanJ. C.MyersC. W.DuranJ. L. (2012). Theoretical underpinnings of interactive team cognition. Theories of team cognition: Cross-disciplinary perspectives, 187-207.
6.
DuhiggC. (2016). What Google learned from its quest to build the perfect team. The New York Times Magazine, 26, 2016.
7.
FioreS. M.RosenM. A.Smith-JentschK. A.SalasE.LetskyM.WarnerN. (2010). Toward an understanding of macrocognition in teams: predicting processes in complex collaborative contexts. Human Factors, 52(2), 203-224.
8.
GrahamJ.SchneiderM.BauerA.BessiereK.GonzalezC. (2004, September). Shared mental models in military command and control organizations: Effect of social network distance. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 48, No. 3, pp. 509-512). Sage CA: Los Angeles, CA: SAGE Publications.
9.
HinszV. B.TindaleR. S.VollrathD. A. (1997). The emerging conceptualization of groups as information processors. Psychological bulletin, 121(1), 43.
10.
HollenbeckJ. R.IlgenD. R.LePineJ. A.ColquittJ. A.HedlundJ. (1998). Extending the multilevel theory of team decision making: Effects of feedback and experience in hierarchical teams. Academy of Management Journal, 41(3), 269-282.
11.
LaskeyK. B.HansonR.TwardyC. (2015, July). Combinatorial prediction markets for fusing information from distributed experts and models. In 2015 18th International Conference on Information Fusion (Fusion) (pp. 1892-1898). IEEE.
12.
LetskyM.WarnerN.FioreS. M.RosenM.SalasE. (2007). Macrocognition in complex team problem solving. OFFICE OF NAVAL RESEARCH ARLINGTON VA.
13.
MaloneT. W. (2018). Superminds: The Surprising Power of People and Computers Thinking Together. Little, Brown.
14.
MaloneT. W.LaubacherR.DellarocasC. (2009). Harnessing crowds: Mapping the genome of collective intelligence.
15.
MathieuJ. E.HeffnerT. S.GoodwinG. F.SalasE.Cannon-BowersJ.A. (2000). The influence of shared mental models on team process and performance. Journal of applied psychology, 85(2), 273.
16.
McNeeseN. J.ReddyM. C.FriedenbergE. M. (2014, September). Towards a team mental model of collaborative information seeking during team decision-making. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 58, No. 1, pp. 335-339). Sage CA: Los Angeles, CA: SAGE Publications.
17.
McNeeseN. J.CookeN. J. (2016, July). Team Cognition as a Mechanism for Developing Collaborative and Proactive Decision Support in Remotely Piloted Aircraft Systems. In International Conference on Augmented Cognition (pp. 198-209). Springer, Cham.
18.
MellersB.StoneE.MurrayT.MinsterA.RohrbaughN.BishopM.… UngarL. (2015). Identifying and cultivating superforecasters as a method of improving probabilistic predictions. Perspectives on Psychological Science, 10(3), 267-281.
19.
OrlitzkyM.HirokawaR. Y. (2001). To err is human, to correct for it divine: A meta-analysis of research testing the functional theory of group decision-making effectiveness. Small Group Research, 32(3), 313-341.
20.
PageL.ClemenR. T. (2012). Do Prediction Markets Produce Well-Calibrated Probability Forecasts?. The Economic Journal, 123(568), 491-513.
21.
RosenbergL.LungrenM.HalabiS.WillcoxG.BaltaxeD.LyonsM. (2018, November). Artificial swarm intelligence employed to amplify diagnostic accuracy in radiology. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)(pp. 1186-1191). IEEE.
22.
RouseW. B.MorrisN. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological bulletin, 100(3), 349.
23.
SalasE.DickinsonT. L.ConverseS. A.TannenbaumS. I. (1992). Toward an understanding of team performance and training.
24.
SalasE.NicholsD. R.DriskellJ. E. (2007). Testing three team training strategies in intact teams: A meta-analysis. Small Group Research, 38(4), 471-488.
25.
SalasE.DiazGranadosD.KleinC.BurkeC. S.StaglK. C.GoodwinG. F.HalpinS. M. (2008). Does team training improve team performance? A meta-analysis. Human factors, 50(6), 903-933.
26.
SurowieckiJ. (2005). The wisdom of crowds. Anchor.
27.
TetlockP. E.GardnerD. (2016). Superforecasting: The art and science of prediction. Random House.
28.
WegnerD. M. (1987). Transactive memory: A contemporary analysis of the group mind. In Theories of group behavior (pp. 185-208). Springer, New York, NY.
